Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "74" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 25 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 25 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460008 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.876225 | 0.079943 | -0.019112 | -0.570351 | -0.769493 | 2.183490 | -0.688498 | 0.503253 | 0.6672 | 0.6848 | 0.3214 | nan | nan |
| 2460007 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.487656 | 0.445009 | -0.223207 | -0.268683 | -0.683698 | 1.026788 | -1.443063 | 3.711735 | 0.6351 | 0.6470 | 0.3438 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 89.06% | 85.63% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1319 | 0.1616 | 0.0517 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.879261 | -0.252030 | 0.306471 | -0.870850 | -0.601645 | 1.298811 | -0.561626 | 4.470152 | 0.6268 | 0.6391 | 0.3739 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.654576 | 0.166427 | 0.101643 | -0.235395 | -0.553868 | 1.305885 | -0.629878 | 4.412259 | 0.6420 | 0.6563 | 0.3775 | nan | nan |
| 2459996 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.083364 | 0.285327 | -0.123543 | -0.335109 | -0.537313 | 1.636108 | -1.065580 | 0.611337 | 0.6466 | 0.6555 | 0.3917 | nan | nan |
| 2459995 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.888607 | 0.129110 | -0.109051 | -0.627075 | -0.400969 | 1.836452 | -1.072476 | 1.276875 | 0.6430 | 0.6550 | 0.3746 | nan | nan |
| 2459994 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.749562 | -0.007685 | 0.428777 | -0.865025 | -0.572817 | 0.928103 | -1.387774 | 1.461166 | 0.6366 | 0.6475 | 0.3742 | nan | nan |
| 2459993 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.061593 | 0.414240 | 0.135672 | -0.443775 | 0.044868 | 2.168280 | -0.997641 | 1.900106 | 0.6322 | 0.6561 | 0.3827 | nan | nan |
| 2459991 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.345299 | 0.013029 | 0.013713 | -0.344819 | -0.174036 | 1.446803 | -1.024383 | 2.226698 | 0.6413 | 0.6442 | 0.3776 | nan | nan |
| 2459990 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.980030 | -0.053736 | 0.008115 | -0.414566 | 0.125767 | 1.098833 | -0.486294 | 3.381127 | 0.6445 | 0.6492 | 0.3766 | nan | nan |
| 2459989 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.929429 | -0.135067 | 0.225930 | -0.136830 | -0.019081 | 1.122034 | -0.603430 | 2.631396 | 0.6401 | 0.6476 | 0.3798 | nan | nan |
| 2459988 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.096476 | -0.318882 | 0.005630 | -0.625800 | -0.033105 | 1.697741 | -0.874767 | 3.028135 | 0.6340 | 0.6415 | 0.3730 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.478053 | 0.119238 | -0.034688 | -0.399727 | -0.206639 | 1.309626 | 0.219750 | 4.533075 | 0.6441 | 0.6518 | 0.3674 | nan | nan |
| 2459986 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.675309 | 0.181181 | -0.015865 | -0.568146 | -0.162129 | 1.618217 | -0.426019 | 1.787351 | 0.6516 | 0.6648 | 0.3242 | nan | nan |
| 2459985 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.571204 | 0.071660 | -0.123291 | -0.412632 | -0.685193 | 1.325732 | -1.124588 | 3.068185 | 0.6319 | 0.6380 | 0.3704 | nan | nan |
| 2459984 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.265699 | 0.447142 | -0.005243 | -0.514688 | -0.431358 | 1.891020 | 0.739570 | 2.237432 | 0.6536 | 0.6601 | 0.3519 | nan | nan |
| 2459983 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.796750 | 0.060741 | -0.067891 | -0.525677 | -0.172419 | 1.425663 | -0.167067 | 2.407142 | 0.6624 | 0.6840 | 0.3128 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.181640 | -0.703788 | -0.483359 | -0.139204 | 0.310467 | 1.240890 | -0.940039 | 0.221580 | 0.7144 | 0.7203 | 0.2801 | nan | nan |
| 2459981 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.212941 | -0.115192 | 0.036705 | -0.665046 | 0.012510 | 1.031769 | -0.838602 | 3.394905 | 0.6408 | 0.6489 | 0.3694 | nan | nan |
| 2459980 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.982933 | 0.046763 | -0.257635 | -0.455464 | -0.076487 | 0.260159 | -0.914945 | 0.018572 | 0.6852 | 0.6966 | 0.3007 | nan | nan |
| 2459979 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.346506 | -0.050870 | -0.157316 | -0.496528 | 0.232185 | 1.157927 | -0.608923 | 2.854996 | 0.6310 | 0.6426 | 0.3696 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.341501 | -0.005617 | -0.023707 | -0.621417 | 0.183805 | 1.616103 | -0.034888 | 5.597204 | 0.6295 | 0.6404 | 0.3753 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.992938 | 0.259956 | -0.148609 | -0.501270 | 0.951021 | 1.727730 | -0.132002 | 4.341197 | 0.6015 | 0.6143 | 0.3419 | nan | nan |
| 2459976 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.245846 | 0.045627 | -0.152972 | -0.589195 | 0.083309 | 1.168172 | -0.633855 | 2.969696 | 0.6454 | 0.6539 | 0.3667 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Variability | 2.183490 | 0.079943 | -0.876225 | -0.570351 | -0.019112 | 2.183490 | -0.769493 | 0.503253 | -0.688498 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 3.711735 | -0.487656 | 0.445009 | -0.223207 | -0.268683 | -0.683698 | 1.026788 | -1.443063 | 3.711735 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 4.470152 | -0.879261 | -0.252030 | 0.306471 | -0.870850 | -0.601645 | 1.298811 | -0.561626 | 4.470152 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 4.412259 | -0.654576 | 0.166427 | 0.101643 | -0.235395 | -0.553868 | 1.305885 | -0.629878 | 4.412259 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Variability | 1.636108 | -0.083364 | 0.285327 | -0.123543 | -0.335109 | -0.537313 | 1.636108 | -1.065580 | 0.611337 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Variability | 1.836452 | -0.888607 | 0.129110 | -0.109051 | -0.627075 | -0.400969 | 1.836452 | -1.072476 | 1.276875 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.461166 | -0.749562 | -0.007685 | 0.428777 | -0.865025 | -0.572817 | 0.928103 | -1.387774 | 1.461166 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Variability | 2.168280 | -1.061593 | 0.414240 | 0.135672 | -0.443775 | 0.044868 | 2.168280 | -0.997641 | 1.900106 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 2.226698 | -1.345299 | 0.013029 | 0.013713 | -0.344819 | -0.174036 | 1.446803 | -1.024383 | 2.226698 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 3.381127 | -0.053736 | -0.980030 | -0.414566 | 0.008115 | 1.098833 | 0.125767 | 3.381127 | -0.486294 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 2.631396 | -0.135067 | -0.929429 | -0.136830 | 0.225930 | 1.122034 | -0.019081 | 2.631396 | -0.603430 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 3.028135 | -0.318882 | -1.096476 | -0.625800 | 0.005630 | 1.697741 | -0.033105 | 3.028135 | -0.874767 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 4.533075 | -0.478053 | 0.119238 | -0.034688 | -0.399727 | -0.206639 | 1.309626 | 0.219750 | 4.533075 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.787351 | 0.181181 | -0.675309 | -0.568146 | -0.015865 | 1.618217 | -0.162129 | 1.787351 | -0.426019 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 3.068185 | 0.071660 | -0.571204 | -0.412632 | -0.123291 | 1.325732 | -0.685193 | 3.068185 | -1.124588 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 2.237432 | -0.265699 | 0.447142 | -0.005243 | -0.514688 | -0.431358 | 1.891020 | 0.739570 | 2.237432 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 2.407142 | -0.796750 | 0.060741 | -0.067891 | -0.525677 | -0.172419 | 1.425663 | -0.167067 | 2.407142 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Variability | 1.240890 | -1.181640 | -0.703788 | -0.483359 | -0.139204 | 0.310467 | 1.240890 | -0.940039 | 0.221580 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 3.394905 | -0.115192 | -1.212941 | -0.665046 | 0.036705 | 1.031769 | 0.012510 | 3.394905 | -0.838602 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Variability | 0.260159 | 0.046763 | -0.982933 | -0.455464 | -0.257635 | 0.260159 | -0.076487 | 0.018572 | -0.914945 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 2.854996 | -1.346506 | -0.050870 | -0.157316 | -0.496528 | 0.232185 | 1.157927 | -0.608923 | 2.854996 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 5.597204 | -0.005617 | -1.341501 | -0.621417 | -0.023707 | 1.616103 | 0.183805 | 5.597204 | -0.034888 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 4.341197 | -0.992938 | 0.259956 | -0.148609 | -0.501270 | 0.951021 | 1.727730 | -0.132002 | 4.341197 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | N05 | RF_maintenance | nn Temporal Discontinuties | 2.969696 | 0.045627 | -1.245846 | -0.589195 | -0.152972 | 1.168172 | 0.083309 | 2.969696 | -0.633855 |